Tree-mendous Vancouver: A Deep Dive into the Blossom Streets!

UBC Key Capabilities in Data Science; Data Visualization Final Project

Author: Eric Hsieh

Introduction

Vancouver, unlike other major cities that boast iconic skylines, is renowned for its natural beauty. The city is a tapestry of beaches, neighborhood areas lush with greenery, and towering mountains that frame its skyline. Among its most celebrated features are the majestic trees that line the streets and the stunning cherry blossoms that adorn neighborhoods every spring.

As a photography enthusiast, I've often marveled at the captivating images of Vancouver's cherry blossoms shared online. However, I've also encountered the challenge of pinpointing the best locations to capture these scenes myself. This project is driven by my passion for both the natural beauty of Vancouver and photography, aiming to analyze the distribution of street trees and cherry blossoms across the city.

Questions of interests

1. Which Vancouver neighbourhood has the most trees?
2. What is Vancouver's top 10 most common tree species?
3. How are cherry blossom trees distributed across Vancouver neighbourhoods?
4. What is the relationship of trees' height and diameter?

Importing Data

This project will be using a subset of the Vancouver Street Trees dataset with 5,000 rows. We will be using the Vancouver city geojson using a URL from Vancouver Data Portal for geographical visualizations with Altair.

import altair as alt
import pandas as pd
import os
import json
#alt.data_transformers.enable('data_server')

Data Description

The Vancouver Street trees dataset is said to include "a listing of public trees on boulevards in the City of Vancouver and provides data on tree coordinates, species and other related characteristics. Park trees and private trees are not included in the inventory. Data currency: The dataset refreshes daily on weekdays. Tree attributes are updated on a regular basis but it may be several years between updates for some attributes. Priorities and resources determine how fast a change in reality is reflected in the data."

A description for columns in the dataset that is needed for explanation.
Some columns are self-explanatory from its column name (e.g. neighbourhood_name is for neighbourhood name):

Column	Description
ON_STREET	The name of the street at which the tree is physically located on
SPECIES_NAME	species name
NEIGHBOURHOOD_NAME	neighbourhood name
DATE_PLANTED	Date is in YYYY-MM-DD format. Planted date of new trees is added after every planting season, usually at the beginning of January and June. Note: Data for this field may not be available for all trees.
DIAMETER	DBH in inches (DBH stands for diameter of tree at breast height)
GENUS_NAME	genus name
COMMON_NAME	common name
HEIGHT_RANGE_ID	0-10 for every 10 feet (e.g., 0 = 0-10 ft, 1 = 10-20 ft, 2 = 20-30 ft, and 10 = 100+ ft)
ROOT_BARRIER	Root barrier installed (Y = Yes, N = No)

Before going into the data analysis and visualization, let's filter the dataframe with only the columns that will be used.
Here's a snapshot of how the dataset currently looks like:

vancity_trees = pd.read_csv('small_unique_vancouver__20240624182849.csv')
vancity_trees.info()
vancity_trees.head(5)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Unnamed: 0          5000 non-null   int64  
 1   std_street          5000 non-null   object 
 2   on_street           5000 non-null   object 
 3   species_name        5000 non-null   object 
 4   neighbourhood_name  5000 non-null   object 
 5   date_planted        2363 non-null   object 
 6   diameter            5000 non-null   float64
 7   street_side_name    5000 non-null   object 
 8   genus_name          5000 non-null   object 
 9   assigned            5000 non-null   object 
 10  civic_number        5000 non-null   int64  
 11  plant_area          4950 non-null   object 
 12  curb                5000 non-null   object 
 13  tree_id             5000 non-null   int64  
 14  common_name         5000 non-null   object 
 15  height_range_id     5000 non-null   int64  
 16  on_street_block     5000 non-null   int64  
 17  cultivar_name       2658 non-null   object 
 18  root_barrier        5000 non-null   object 
 19  latitude            5000 non-null   float64
 20  longitude           5000 non-null   float64
dtypes: float64(3), int64(5), object(13)
memory usage: 820.4+ KB

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
0	10747	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	...	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
2	29676	ROSS ST	ROSS ST	NIGRA	Sunset	NaN	12.0	ODD	PINUS	N	...	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
4	21098	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaN	15.5	ODD	AESCULUS	Y	...	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958

5 rows × 21 columns

This observation sees 5000 data entries, 21 total columns consisting of 8 numerical columns (float64 and int64). We're seeing some columns doesn't have 5000 entries indicating there are null values. Let's observe how many null values there are.

# Note: analysis on latitude and longitude aren't useful.
trees_df = vancity_trees[[
        "on_street",
        "species_name",
        "neighbourhood_name",
        "date_planted",
        "diameter",
        "genus_name",
        "common_name",
        "height_range_id",
        "root_barrier",
        "latitude",
        "longitude"
]]
trees_df
trees_df.describe()

	diameter	height_range_id	latitude	longitude
count	5000.000000	5000.00000	5000.000000	5000.000000
mean	12.340888	2.73440	49.247349	-123.107128
std	9.266600	1.56957	0.021251	0.049137
min	0.000000	0.00000	49.202783	-123.220560
25%	4.000000	2.00000	49.230152	-123.144178
50%	10.000000	2.00000	49.247981	-123.105861
75%	18.000000	4.00000	49.263275	-123.063484
max	71.000000	9.00000	49.293930	-123.023311

trees_df.describe(exclude='number', datetime_is_numeric=True)

	on_street	species_name	neighbourhood_name	date_planted	genus_name	common_name	root_barrier
count	5000	5000	5000	2363	5000	5000	5000
unique	607	171	22	1599	67	361	2
top	CAMBIE ST	SERRULATA	Renfrew-Collingwood	2004-02-16	ACER	KWANZAN FLOWERING CHERRY	N
freq	49	463	384	7	1218	383	4679

Question 1: Which Vancouver neighbourhood has the most trees?

A skeleton map of Vancouver will be generated first before we map the data and label the neighbourhoods.

trees_geojson = url_geojson = 'https://raw.githubusercontent.com/UBC-MDS/exploratory-data-viz/main/data/local-area-boundary.geojson'

data_geojson = alt.Data(url=trees_geojson, format=alt.DataFormat(property='features',type='json'))
data_geojson

Data({
  format: DataFormat({
    property: 'features',
    type: 'json'
  }),
  url: 'https://raw.githubusercontent.com/UBC-MDS/exploratory-data-viz/main/data/local-area-boundary.geojson'
})

vancouver_map_outline = alt.Chart(data_geojson).mark_geoshape(
    color = 'lightblue', 
    opacity= 0.5, 
    stroke='black').encode(
).project(type='identity', reflectY=True)

vancouver_map_outline

scatter_plot = alt.Chart(vancity_trees).mark_circle(
    size=60, 
    opacity=0.5
).encode(
    x=alt.X('longitude:Q', title='Longitude', scale=alt.Scale(zero=False)),
    y=alt.Y('latitude:Q', title='Latitude', scale=alt.Scale(zero=False)),
    color=alt.Color(
        'diameter:Q', 
        title='Tree Diameter',
        scale=alt.Scale(scheme='greens')  # Apply green color scheme
    ),
    tooltip=['species_name', 'neighbourhood_name', 'diameter']
).properties(
    width=550,
    height=400,
    title='Scatter Plot of Trees in Vancouver by Longitude and Latitude'
)

vancouver_map_outline + scatter_plot

This scatter plot shows the location of all the trees. However, this graph doesn't give us much useful information at first glance.

count_df = trees_df.groupby('neighbourhood_name')['neighbourhood_name'].count().reset_index(name='tree_count')
points_df = trees_df.groupby('neighbourhood_name')['longitude','latitude'].median()#.reset_index()
counts_df = count_df.merge(points_df, on ='neighbourhood_name')

<ipython-input-8-a4812a868b61>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  points_df = trees_df.groupby('neighbourhood_name')['longitude','latitude'].median()#.reset_index()

van_circle_size = (
    alt.Chart(counts_df).mark_circle().encode(
        longitude='longitude',
        latitude='latitude',
        size='tree_count:Q',
        color=alt.Color('tree_count:Q', title='Tree count'),
        tooltip=['name:N', alt.Tooltip('tree_count:Q', title='Tree counts')],
    )
    .project(type='identity', reflectY=True)
    .properties(height=200, width=400, title='Vancouver neighbourhoods')
)
van_map_points = vancouver_map_outline + van_circle_size
van_map_points

This visualization is informative as the size and shade of the circle is readable, but there is a much better way to communicate this data.

click = alt.selection_single(fields=['neighbourhood_name'])

title = alt.TitleParams(
    'Figure 1. Vancouver tree counts and Cherry Blossom distribution',
    subtitle='Renfrew-Collingwood has the most trees. Hover over neighbourhoods to see its total amount of trees',
)

vancouver_map = (
    alt.Chart(data_geojson)
    .mark_geoshape()
    .transform_lookup(
        lookup='properties.name',
        from_=alt.LookupData(counts_df, 'neighbourhood_name', ['tree_count', 'neighbourhood_name']),
    )
    .encode(
        color=alt.Color('tree_count:Q', title=' Tree count'),
        tooltip=['neighbourhood_name:N', alt.Tooltip('tree_count:Q', title='Tree counts')],
    )
    .add_selection(click)
    .project(type='identity', reflectY=True)
    .properties(title=title, height=400, width=700,)
)
vancouver_map

The choropleth map is a much better method to convey this type of information for area density. At a glance, we can quickly distinguish the area with dense trees by the shade of the colour.

Now, before graphing the cherry trees data points, we need to filter and defines the ones we want to display. I'm choosing to visualize the top 6 most popular cherry trees in Vancouver.

cherry_trees = trees_df[trees_df['common_name'].str.contains('CHERRY')]

# Vancouver top 6 most populary cherry trees
cherry_trees_6 = (
    cherry_trees.groupby('common_name')['common_name']
    .count()
    .reset_index(name='count')
    .sort_values(by='count', ascending=False).iloc[:6,0].tolist()
)
cherry_trees = cherry_trees[cherry_trees['common_name'].isin(cherry_trees_6)]
cherry_trees

	on_street	species_name	neighbourhood_name	date_planted	diameter	genus_name	common_name	height_range_id	root_barrier	latitude	longitude
21	E 49TH AV	SERRULATA	Sunset	NaN	14.0	PRUNUS	KWANZAN FLOWERING CHERRY	3	N	49.225494	-123.087200
42	W 35TH AV	SERRULATA	Shaughnessy	NaN	11.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.239992	-123.152677
44	VERNON DRIVE	SERRULATA	Strathcona	NaN	22.0	PRUNUS	UKON JAPANESE CHERRY	3	N	49.277064	-123.079379
60	CAMOSUN ST	SERRULATA	Dunbar-Southlands	NaN	16.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.246430	-123.196900
62	CAROLINA ST	SERRULATA	Mount Pleasant	NaN	12.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.261203	-123.091148
...	...	...	...	...	...	...	...	...	...	...	...
4978	DUMFRIES ST	SERRULATA	Victoria-Fraserview	NaN	24.0	PRUNUS	KWANZAN FLOWERING CHERRY	3	N	49.220258	-123.074637
4988	W 16TH AV	SERRULATA	Shaughnessy	NaN	7.0	PRUNUS	KWANZAN FLOWERING CHERRY	1	N	49.257003	-123.130106
4989	E 35TH AV	SERRULATA	Kensington-Cedar Cottage	NaN	27.0	PRUNUS	UKON JAPANESE CHERRY	4	N	49.238311	-123.087098
4992	E 53RD AV	SERRULATA	Victoria-Fraserview	NaN	20.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.221161	-123.060833
4995	E 53RD AV	SERRULATA	Victoria-Fraserview	NaN	17.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.221161	-123.061023

541 rows × 11 columns

Now we add the neighbourhoods label and include the cherry tree points on click.

# Add Labels Layer
labels = (
    alt.Chart(counts_df)
    .mark_text()
    .encode(
        longitude='longitude',
        latitude='latitude',
        text='neighbourhood_name:N',
        size=alt.value(10),
        opacity=alt.value(1),
    )
    .project(type='identity', reflectY=True)
    .properties(height=400, width=700, title='Vancouver map')
)

# Cherry tree points layer
cherry_trees_layer = (
    alt.Chart(cherry_trees)
    .mark_circle(size=30, color='pink', opacity=0.9)
    .encode(
        longitude='longitude:Q',
        latitude='latitude:Q',
        color=alt.Color('common_name:N', title='Cherry tree types', scale=alt.Scale(
                range=['#ffdede', '#FF69B4', '#882238', '#D1C4E9', '#616c99', '#505477']
            ), legend=None),
        tooltip=['common_name:N', 'neighbourhood_name:N', 'date_planted:N']
    )
    .transform_filter(click)  # Filter points based on selected neighbourhood
    .project(type='identity', reflectY=True)
)

vancouver_map = vancouver_map + cherry_trees_layer + labels
vancouver_map

This map looks good with points coloured to resemble the different types of cherry trees.

import pandas as pd
import plotly.express as px

# Load the data
df = pd.read_csv('small_unique_vancouver__20240624182849.csv', sep=',')

# Getting neighborhood totals
df2 = pd.merge(
    df[['neighbourhood_name']].value_counts().reset_index().rename(columns={'neighbourhood_name': 'NEIGHBOURHOOD_NAME', 0: 'count'}),
    df[['neighbourhood_name', 'longitude', 'latitude']].groupby('neighbourhood_name').mean().reset_index().rename(columns={'neighbourhood_name': 'NEIGHBOURHOOD_NAME'})
)

# Drawing the figure
fig = px.scatter_mapbox(
    df2,
    lat='latitude',
    lon='longitude',
    color='count',
    opacity=0.5,
    center=dict(lon=df2['longitude'].mean(), lat=df2['latitude'].mean()),
    zoom=10,
    size_max=15
)

fig.update_layout(
    mapbox_style='open-street-map',
    width=600,
    height=400
)

fig

Although this visualization method above hasn't been taught in this course, this implementation could be further explored for a future project. I will need to figure out how to accurately plot the Vancouver neighbourhoods boundary points to overlay onto this detailed map of Vancouver. For the sake of saving time and the aim for minimalistic visualization, I will continue with the choropleth map.

The choropleth map of Vancouver's neighborhoods shows the number of trees in each area. The map is colored according to tree count, with a colour gradient ranging from light yellow (fewer trees) to dark blue (more trees). From the map, we can see that Renfrew-Collingwood neighbourhood is highlighted as having the highest number of trees at 384 counts. The two neighbourhoods besides Renfrew-Collingwood comes close at second place for Kensington-Cedar Cottage with 375 trees and third place for Hastings-Sunrise with 340 trees. The Strathacona neighbourhood has the least amount of tree with a count of 75.

Question 2: What is Vancouver's top 10 most common tree species?

I want to have an interative bar graph with the vancouver map of the most common trees for each neighbourhoods on click. By default, it shows the most common trees for Vancouver city (all neighbourhoods combined).

# Hold shift to select multiple neighbourhoods
click = alt.selection_multi(fields=['neighbourhood_name'])

vancouver_map_interactive = vancouver_map.encode(
    opacity=alt.condition(click, alt.value(1), alt.value(0.3))
).add_selection(click)

# slider bar to control the number of trees to display
slider = alt.binding_range(
    name='Number of popular trees to display: ',
    step=1,
    min=5,
    max=20)

select_trees = alt.selection_single(
    fields=['num_names'], init={'num_names': 20}, bind = slider)

# Bar Chart
title = alt.TitleParams(
    'Figure 2. Most popular trees in selected neighbourhoods',
    subtitle='Kwanzan Flowering Cherry is the most popular tree in Vancouver',
)
common_tree_names = (
    alt.Chart(trees_df).transform_filter(click).mark_bar().encode(
        x=alt.X('count:Q', title=''),
        y=alt.Y('common_name:N', title='', sort='-x'),
        color=alt.value('#4A993A')
    )
    .transform_aggregate(count='count()', groupby=['common_name'])
    .transform_window(
        rank='rank(count)', sort=[alt.SortField('count', order='descending')]
    )
    .transform_filter(alt.datum.rank <= select_trees.num_names)
    .properties(title=title, height=400, width=300)
    .add_selection(click)
    .add_selection(select_trees)
)

vancouver_map_interactive | common_tree_names

From this graph, we can see the top 5 Vancouver's most common trees are Kawanzan Flowering Cherry, Pissard Plum, Norway Maple, Crimean Linden, and the Pryamidal European Hornbeam. It is interesting to see that the Vancouver West Side neighbourhoods doesn't even have Pryamidal European Hornbeam ranked in the top 20. Vancouver West Side has American Elm ranked 4th most popular and Crimean Linden ranked 5th most popular.

The Kwanzan Flowering Cherry has been consistently the top 3 most common trees for Vancouver neighbourhoods.

Question 3: How are cherry blossom trees distributed across Vancouver neighbourhoods?

This is the most exciting part of the Vancouver tree analysis! How are the beautiful cherry blossom tress distributed across Vancouver city? I will visualize this using bar chart interacting with the Vancouver map on click.

# The most popular cherry trees
cherry_trees_6 = (
    cherry_trees.groupby('common_name')['common_name']
    .count()
    .reset_index(name='count')
    .sort_values(by='count', ascending=False).iloc[:6,0].tolist()
)
cherry_trees = cherry_trees[cherry_trees['common_name'].isin(cherry_trees_6)]
cherry_trees

	on_street	species_name	neighbourhood_name	date_planted	diameter	genus_name	common_name	height_range_id	root_barrier	latitude	longitude
21	E 49TH AV	SERRULATA	Sunset	NaN	14.0	PRUNUS	KWANZAN FLOWERING CHERRY	3	N	49.225494	-123.087200
42	W 35TH AV	SERRULATA	Shaughnessy	NaN	11.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.239992	-123.152677
44	VERNON DRIVE	SERRULATA	Strathcona	NaN	22.0	PRUNUS	UKON JAPANESE CHERRY	3	N	49.277064	-123.079379
60	CAMOSUN ST	SERRULATA	Dunbar-Southlands	NaN	16.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.246430	-123.196900
62	CAROLINA ST	SERRULATA	Mount Pleasant	NaN	12.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.261203	-123.091148
...	...	...	...	...	...	...	...	...	...	...	...
4978	DUMFRIES ST	SERRULATA	Victoria-Fraserview	NaN	24.0	PRUNUS	KWANZAN FLOWERING CHERRY	3	N	49.220258	-123.074637
4988	W 16TH AV	SERRULATA	Shaughnessy	NaN	7.0	PRUNUS	KWANZAN FLOWERING CHERRY	1	N	49.257003	-123.130106
4989	E 35TH AV	SERRULATA	Kensington-Cedar Cottage	NaN	27.0	PRUNUS	UKON JAPANESE CHERRY	4	N	49.238311	-123.087098
4992	E 53RD AV	SERRULATA	Victoria-Fraserview	NaN	20.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.221161	-123.060833
4995	E 53RD AV	SERRULATA	Victoria-Fraserview	NaN	17.0	PRUNUS	KWANZAN FLOWERING CHERRY	2	N	49.221161	-123.061023

541 rows × 11 columns

title = alt.TitleParams(
               'Figure 3. Cherry trees in selected neighbourhoods',
    subtitle=['Renfrew-Collingwood has the most number of cherry trees','downtown vancouver has the least (clickable)'],
)

neighbourhood_cherry_trees = (
    alt.Chart(cherry_trees, title=title).mark_bar().encode(
        alt.X('count()'),
        alt.Y('neighbourhood_name:N', sort=alt.SortField(field='neighbourhood_name', order='ascending'), title=''),
        color=alt.Color('common_name:N', title='Cherry tree types', scale=alt.Scale(
                range=['#ffdede', '#FF69B4', '#882238', '#D1C4E9', '#616c99', '#505477']
            ), legend=alt.Legend(title='Cherry Tree Types', orient='right')),
        opacity=alt.condition(click, alt.value(1), alt.value(0.3)),
    )
    .add_selection(click)
    .properties(height=400, width=300)
)

(vancouver_map_interactive | neighbourhood_cherry_trees)

During springtime in Vancouver, the neighbourhood with the most cherry blossoms are Renfrew Collingwood, Mount Pleasant, and Dunbar-Southlands.

Arbutus-Ridge and Downtown only has 2 varieties of cherry blossoms, whereas in Grandview-Woodland and Mount Pleasant you will find a great variety of cherry blossom.

Question 4: What is the Relationship of trees' height and diameter?

We have two numerical data columns in our dataset; tree height and diameter. We will use the top 25 most common trees to answer this question. A scatterplot will be graphed with a visualization on the mean diameter line and a dropdown menu to view the "heigh and diameter" relationship of different trees.

top_trees = (
    trees_df['common_name']
    .value_counts()[:20]
    .sort_values(ascending=False)
    .reset_index(name='count')
)

tree_names = sorted(top_trees['index'].unique())
dropdown_menu = alt.binding_select(
    name='Select tree type to view its height and diameter relationship: ',
    options=tree_names,
)
select_tree = alt.selection_single(fields=['common_name'], bind=dropdown_menu)

# Graphing the scatter plot
tree_scatter_plot = (alt.Chart(trees_df).mark_circle().encode(alt.X('diameter', title='Diameter (inch)'), alt.Y('height_range_id'))
).transform_filter(select_tree)

plot_line_title = alt.TitleParams(
               'Figure 4. Height and diameter relationship of popular Vancouver trees',
    subtitle=['The line indicating the mean diameter over height ranges.'],
)

tree_line_plot = (
    alt.Chart(trees_df, title=plot_line_title).mark_line(color='hotPink').encode(
        alt.X('mean(diameter)'),
        alt.Y('height_range_id', title='Height range ID'),
        tooltip=alt.value('Mean of diameter'),
    ).properties(height = 200, width = 800)
).transform_filter(select_tree)
tree_size = tree_line_plot + tree_scatter_plot
##
vancouver_map_interactive |(tree_line_plot + tree_scatter_plot).add_selection(click)

tree_size = tree_size.add_selection( click).add_selection(click).add_selection(select_tree).transform_filter(select_tree)
tree_size

The default view shows the data points of all the top 20 common Vancouver trees. We can see there is a general positive relationship between tree height and diameter. For all the tree species, we can also see a general positive relationship betwen tree height and diameter. Due to the lack of data for the tallest tree of each species, sometimes the data trend skews to a negative correlation at the higher "height range ID."

Dicussion & Conclusion

The trees of Vancouver not only provides the city with a beautiful scene, but it has a functional purpose to boost health and wellness such as clean air, provide shading, and creates shelter for wild animals. This data analysis provides a detailed exploration of the distribution and charatersitcs of Vancouver's street trees with a focus on cherry blossoms.

The map highlights the Renfrew-Collingwood neighborhood as having the highest number of trees, with a total count of 384. Kensington-Cedar Cottage and Hastings-Sunrise follow closely, with 375 and 340 trees. In contrast, the Strathcona neighborhood has the least number of trees, with only 75 counted. This distribution suggests a significant variation in tree density across Vancouver, with certain neighborhoods having a much higher concentration of trees.

Vancouver's top 5 most common trees are the Kwanzan Flowering Cherry, Pissard Plum, Norway Maple, Crimean Linden, and Pyramidal European Hornbeam. Using the multi-selection tool for multiple neighbourhoods, it is noteworthy to point out that the Vancouver West Side neighborhoods (borders from the west of Mount Pleasant, Riley Park, and Sunset) do not even rank the Pyramidal European Hornbeam in their top 20 most common trees. Instead, these neighborhoods have the American Elm as the 4th most popular and the Crimean Linden as the 5th most popular tree. This observation highlights the regional variations in tree popularity within Vancouver.

The Kwanzan Flowering Cherry consistently ranks among the top 3 most common trees in Vancouver neighborhoods. This consistency showcases the tree's popularity and its significance in the Vancouver urban landscape in the spring season. During springtime, the neighborhoods with the most cherry blossoms are Renfrew-Collingwood, Mount Pleasant, and Dunbar-Southlands. The variety of cherry blossoms is also notable, with Grandview-Woodland and Mount Pleasant offering a wide range of cherry blossom species. In contrast, Arbutus-Ridge and Downtown only feature 2 varieties, which may reflect different urban planting strategies. Different cherry blossom species have different blossoming times and duration. It would be interesting to do analysis to see if cherry blossom blooming time have a relationship to planting stragegies.

In conclusion, this analysis of Vancouver street trees reveals insights into the distribution and characteristics to the city's urban forest planning. The data shows a widespread of cherry blossom trees particularly in neighborhoods like Renfrew-Collingwood, Mount Pleasant, and Dunbar-Southlands, which are rich in variety and tree density.

The observation of tree species popularity between different areas, such as the distinct preferences in Vancouver's West Side neighborhoods, suggest that urban planning and community preferences play a crucial role in shaping the city's tree canopy.

The relationship between tree height and diameter observed in the scatter plot suggests potential avenues for future research, including exploring whether taller trees (as a potential indicator of older neighbourhoods) are more common in wealthier neighborhoods. Future projects could also involve a deeper investigation into the socioeconomic factors influencing tree distribution in Vancouver. Additionally, exploring the environmental and aesthetic impact of tree types in urban areas could provide valuable insights for city planning and conservation efforts.

This analysis not only sheds light on Vancouver's current state of tree distribution, it also lays a foundation for future urban forest planning aimed to enhance the city's natual beauty and sustainability.

Dashboard

alt.themes.enable('none');
(
    vancouver_map_interactive.properties(width = 750)
    & (common_tree_names | neighbourhood_cherry_trees).add_selection(click)
    & tree_size.add_selection(select_tree).transform_filter(select_tree))
# .configure_view(stroke=None)

References

Data Source:
Vancouver Street trees by the City of Vancouver

Article that sparked interest:
CBC - Why Metro Vancouver's cherry blossom trees offer more than just a pretty picture